Prediction and Urban Adaptivity Evaluation Model Based on Carbon Emissions: A Case Study of Six Coastal City Clusters in China

Abstract

Aiming at predicting the issues of social economics, environmental pollution, climate change, and marine disasters influenced by carbon emissions, a predicting model based on carbon emissions with the Random Forest (RF) model was constructed. Meanwhile, a novel urban adaptivity evaluation model is put forward considering the above four domains of indicators; hence, the predicting and evaluation models are integrated. Six coastal city clusters of China are selected as study areas and the result of the RF model with carbon emissions shows that northern city clusters suffer more pollutant loads due to their heavy industry layout; southern cities generally have higher GDP, while they are more vulnerable toward extreme weather and marine disasters. The result of the evaluation system indicates that northern city clusters have higher urban adaptivity (0.49–0.50) due to their balance between economics and pollution as well as less vulnerability to climate change because of their relatively high latitude. On the contrary, southern cities should focus on environmental pollution and tropical storms to pursue superior compatibility.